Abstract
Objectives
Previous studies had reported that frailty and loneliness were associated with increased risk of cardiovascular disease (CVD). The aim of present study was to evaluate the combined effect of frailty and loneliness on the risk of CVD.
Methods
A total of 9,674 participants from the China Health and Retirement Longitudinal Study were included. Multivariate Cox proportional hazards regression model was used to explore the associations between frailty, loneliness and new-onset CVD, stroke and cardiac events.
Results
During the 7-year follow-up, a total of 1,758 respondents experienced CVD (including 584 stroke and 1,324 cardiac events). Compared to those without loneliness or frailty, individuals with loneliness alone, or with frailty alone, or with both loneliness and frailty were significantly associated with increased risk of CVD, with corresponding HRs (95%CIs) were 1.21(1.07–1.37), 1.57(1.32–1.86) and 1.78(1.52–2.10), respectively. Similarly, participants with loneliness alone, or with frailty alone, or with both loneliness and frailty were associated with higher risk of cardiac events. The significant associations were consistent in age subgroups (participants aged less or more than 60 years).
Conclusion
Our study indicated that there was a combined effect of effect of frailty and loneliness on the risk of CVD, stroke and cardiac events. These findings highlighted the importance of identifying loneliness and frailty, and intervening much earlier both in older and younger population.
Key words: Loneliness, frailty, CVD, CHARLS
Introduction
Iodine Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide and responsible for 18.6 million deaths in 2019, which was estimated to account for 32.3% of all-cause global deaths (1, 2, 3). As the population boom of the 20th century ages, age-related diseases, such as CVD, has come to the forefront as emergent health concerns (4).
Frailty, defined as a state of decreased physiological reserve and increased vulnerability to possible intrinsic or extrinsic stressors, is a common geriatric condition closely related to human aging (5, 6). Previous studies have shown that the frailty status is associated with poor health-related outcomes, including falls, functional decline, hospitalization, chronic diseases and mortality (7, 8, 9, 10, 11). Frailty index, one of the widely used measures of biological age, has been recognized as one of the most effective methods for operationalizing frailty (12). Evidence from previous studies had also shown that frailty index is a better predictor of adverse outcomes than DNA methylation (13). Loneliness defined as “a distressing feeling that accompanies the perception that one's social needs are not being met by the quantity or especially the quality of one's social relationships” (14), with reported prevalence of 25% in the world population and 30% in older Chinese adults (15, 16). Loneliness has been observed to be associated with subsequent adverse outcomes, such as poor functional ability, CVD, comorbidity and all-cause mortality (17, 18, 19).
Association between frailty and loneliness had been widely investigated. In a systematic review and meta-analysis of 16 studies, results indicated frailty is significantly associated with a higher risk of loneliness compared with robustness or non-frailty, and loneliness is associated with a significantly higher risk of worsening frailty (20). However, to our knowledge, there was limit evidence on the cumulative effect of frailty and loneliness on health-related outcomes. Therefore, in the current study, we aimed to evaluate the combined effect of frailty and loneliness on the development of CVD among the middle-aged and elderly Chinese people, based on the data from the China Health and Retirement Longitudinal Study (CHARLS).
Methods
Study population
The present study was embedded in the CHARLS. The survey, established in 2011, is an outgoing nationwide longitudinal ageing cohort, that uses a multistage clustering sample method to select participants and conducted to collect a series of data in China (21, 22). The first visit was accomplished in 2011–2012 (Wave 1) of 17,708 patients, subsequently third follow-up visits carried out after that, each nearly two years apart among survivors (2013–2014: Wave 2, 2015–2016: Wave 3 and 2017–2018: Wave 4). The ethics application for collecting data on human subjects in CHARLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015), and all CHARLS participants provided written informed consent. The details of the CHARLS data are available at its website (http://charls.pku.edu.cn/en).
In current study, we aimed to conduct a longitudinal analysis to investigate the associations between frailty and loneliness on the risk of CVD using data from the four waves of CHARLS. In the present analysis, we included participants who met all of the following criteria: (1) individuals ≥45 years old, (2) individuals with complete information frailty and loneliness in baseline, (3) individuals without reported CVD in baseline, (4) individuals who were successfully followed-up, and with complete information about CVD. Finally, a total of 9,647 individuals were included in the longitudinal analysis (Figure 1).
Figure 1.
Flow chart of sample selection and the exclusion criteria
Assessment of frailty index and loneliness
The frailty index was conducted following a standard procedure (23). Participants with ≥30 items of health deficits were included due to that 30 items are a minimum threshold in various universal standards of the deficit list. According to previous and CHARLS data catalog (24, 25, 26), activity of daily living (ADL) disabilities and instrumental activity of daily living (IADL) disabilities (11 items), physical function limitations (9 items), chronic diseases (9 items), mental health indicators (5 items) and subjective functioning (self-rated health) were included (Table S1). Hypertension, diabetes mellitus, dyslipidemia and body mass index (BMI) were not included due to associated with CVD. For each individual, the frailty index was calculated by summing all health deficits and dividing by the total number of health deficits. Each item was dichotomous or ordinal and measured on a scale of 0 to 1 to represent the severity of health deficits. The frailty index was calculated using unweighted counts of the number of deficits divided by the total possible number of deficits. The frailty index score ranged from 0.00 to 1.00, with a higher value indicating a worse, frailer status. With reference to a previous study among older Chinese adults13, we further categorized the frailty index into three levels of frailty: robust (frailty index score≤0.10), pre-frailty (frailty index score > 0.10 to≤0.25), and frailty (frailty index score ≥0.25).
In current study, loneliness was measured at baseline with a single item included in the Centre for Epidemiological Studies Depression Scale (CESD): ‘In the last week, how often did you feel lonely?'. The 4-point answer scale ranged from ‘never' to ‘always'. Despite concerns that self-reporting with a single item may lead to an underestimation of the true prevalence of loneliness, this single item approach has been widely used (27, 28). Loneliness was dichotomized into two categories (0[not lonely] =felt lonely rarely or none of the time, and 1 [lonely]=felt lonely sometimes, occasionally or most of the time)
Outcome assessments
The primary outcome of the present study was incident CVD (stroke or cardiac events), and the secondary outcomes were stroke and cardiac events, separately. The incident of stroke or cardiac events was defined as new events that occurred from Wave 2 to Wave 4, based on a self-reported physician's diagnosis (“Has a doctor ever told you that you had any heart disease [myocardial infarction, coronary heart disease, angina, congestive heart failure, or other heart problems] or stroke?”. The date of CVD diagnosis was recorded as being between the date of the last interview and that of the interview reporting an incident CVD (29, 30).
Covariates assessments
The covariates were collected at baseline including age, sex, place of residence (rural vs. urban), smoking status (ever smoking vs. never smoking), educational level (illiteracy; primary school; middle school; high school or above), drinking status (ever drinking vs. never drinking), BMI (the weight in kilograms divided by the square of the height in meters), the presence or absence of other chronic diseases (dyslipidemia, chronic lung disease) and medications (anti-hypertensive and anti-dyslipidemic). “Ever smoking” means that the respondent reported smoking at some point, and “never smoking” means that the respondent reported never having smoked. “Ever drinking” means that the respondent reports having had an alcoholic beverage in the past, and “never drinking” means that the respondent reported not having any alcoholic beverage in the past. Blood pressure was measured with an electronic sphygmomanometer (Omron HEM-7200 Monitor) after 5 minutes of rest in the sitting position and was defined as the average of three separate measurements. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥ 90 mm Hg, current use of antihypertensive medications, or self-reported history of hypertension. The blood samples were stored at − 70°C, and measured in a central laboratory in Beijing. Serum creatinine (SCr, mg/dL) was measured by the rate-blanked and compensated Jaffe creatinine method. Glycosylated hemoglobin (HbAlc, %) was measured by boronate affinity HPLC. Low-density lipoprotein (LDL, mg/ dL) were measured by enzymatic colormetric test. Uric acid (UA, mg/dL) were measured by uricase and catalase. Moreover, dyslipidemia was defined as triglycerides ≥ 150 mg/dL, or total cholesterol ≥ 240 mg/dL, or high-density lipoprotein cholesterol < 40 mg/dL, or low-density lipoproteins cholesterol ≥ 160 mg/dL, or current use of the lipid-lowering medications, or self-reported history of dyslipidemia. And diabetes was defined as fasting glucoses 126 mg/dL, or glycosylated hemoglobin (HbAlc) ≥ 6.5%, or treatment for diabetes mellitus, or self-reported history of diabetes.
Statistical analysis
All participants were divided into four groups according to frailty index and loneliness status: robust; loneliness only; frailty only; loneliness and frailty. Participants' baseline characteristics are presented as percentages for categorical variables, as the means with standard deviation for normally distributed variables and as medians with interquartile range for nonnormally distributed variables. Demographic and clinical characteristics were compared between four subgroups by ANOVA or Kruskal–Wallis test for continuous variables and χ2 test for categorical variables. Kaplan–Meier curves and the log-rank test were used to compare the cumulative risk of events among four different groups. We computed hazard ratios (HRs) and 95% confidence intervals (95% CIs) for CVD, stroke and cardiac events by using Cox proportional hazards models. Cox proportional hazards models were performed using three incremental levels of covariate adjustments. Potential covariate, such as age, sex, living place, education level, smoking status, drinking status, BMI, hypertension, diabetes mellitus and dyslipidemia were controlled.
In sensitivity analysis, participants were divided into four groups according to pre-frailty and loneliness. Furthermore, we aimed to evaluate the association between loneliness and frailty with new-onset CVD, stroke and cardiac events, respectively. In addition, we tend to evaluate the association between frailty phenotype and loneliness new-onset CVD, stroke and cardiac events, among participants with age ≥ 60 years (10, 31). In subgroup analyses, considering most studies assess frailty status only in the participants more than 60 years, we examine the association between frailty index and loneliness and CVD in age subgroups (< 60 years vs ≥ 60 years). Furthermore, we performed to evaluate the association between the combined effect of frailty index and loneliness with risk of CVD according to sex, living place, smoking, drinking, diabetes mellitus and BMI subgroups.
Results
In the current study, a total of 9,674 participants (4,830 men and 4,844 women) were included in the analysis, and the average age was 58.04 ± 8.81 years. Among 9,674 participants, 6,437 (66.73%) participants without frailty or loneliness, 1,923 (19.88%) participants reported loneliness alone, 643 (6.55%) participants reported frailty alone, and 671 (6.84%) participants reported both loneliness and frailty. Baseline characteristics, such as age, sex, living place, education level, history of hypertension, smoking, drinking, BMI and DBP were significantly different among the four subgroups (Table 1).
Table 1.
Baseline characteristics of the study participants according to frailty/loneliness status in baseline
| Variable | Robust | Loneliness only | Frailty only | Loneliness and Frailty | P value |
|---|---|---|---|---|---|
| No. of subjects | 6437 | 1923 | 643 | 671 | |
| Age, years | 57.25±8.61 | 57.83±8.80 | 62.36±9.37 | 62.02±9.37 | <0.001 |
| Sex, n (%) | <0.001 | ||||
| Male | 3466(53.84) | 877(45.61) | 252(39.19) | 235(35.02) | |
| Female | 2971(46.16) | 1046(54.39) | 391(60.81) | 436(64.98) | |
| Living place, n (%) | <0.001 | ||||
| Urban | 2596(40.33) | 613(31.88) | 113(17.57) | 125(18.63) | |
| Rural | 3841(59.67) | 1310(68.12) | 530(82.43) | 546(81.37) | |
| Education level, n (%) | <0.001 | ||||
| Illiteracy | 1410(21.90) | 558(29.02) | 271(42.15) | 315(46.94) | |
| Primary school | 2522(39.18) | 799(41.55) | 282(43.86) | 295(43.96) | |
| Middle school | 1571(24.41) | 377(19.60) | 65(10.11) | 51(7.60) | |
| High school or above | 934(14.51) | 189(9.83) | 25(3.89) | 10(1.49) | |
| Medical history, n (%) | |||||
| Hypertension | 1450(22.52) | 364(18.93) | 142(22.08) | 112(16.69) | <0.001 |
| Dyslipidemia | 502(7.80) | 128(6.66) | 46(7.15) | 51(7.60) | 0.401 |
| Diabetes mellitus | 313(4.86) | 91(4.73) | 47(7.31) | 38(5.66) | 0.069 |
| Smoking, n (%) | 2767(43.00) | 729(37.91) | 230(35.77) | 226(33.68) | <0.001 |
| Drinking, n (%) | 2725(42.33) | 762(39.63) | 242(37.64) | 243(36.21) | <0.001 |
| BMI (kg/m2) | 23.40(21.34–25.25) | 23.28(20.74–24.61) | 23.15(20.41–24.98) | 22.89(20.19–23.96) | <0.001 |
| FBG (mg/dL) | 102.24(94.32–112.68) | 102.42(93.96–113.22) | 101.70(93.96–115.38) | 101.34(93.96–111.60) | 0.952 |
| SBP, mmHg | 129.17±19.42 | 127.83±19.40 | 131.34±20.93 | 129.74±23.77 | 0.309 |
| DBP, mmHg | 75.92±11.48 | 75.16±11.20 | 74.50±11.20 | 73.68±11.02 | <0.001 |
BMI: body mass index; FBG: fasting blood glucose; SBP: systolic blood pressure; DBP: diastolic blood pressure; Continuous variables are expressed as mean± standard deviation, or as median (interquartile range). Categorical variables are expressed as frequency (percent).
During the 7-year follow-up, a total of 1,758 respondents experienced CVD (including 584 stroke and 1,324 cardiac events). The cumulative incidence of CVD, stroke and cardiac events were significantly higher in individuals with both loneliness and frailty than those without loneliness or frailty (Figure. 2). After further adjusting for age, sex and other potential risk factors, individuals with loneliness alone, or with frailty alone, or with both loneliness and frailty were significantly associated with increased risk of CVD, with corresponding HRs (95%CIs) were 1.24(1.10–1.40), 1.62(1.37–1.92) and 1.92(1.64–2.27), respectively (Table 2). Similarly, participants with loneliness alone, or with frailty alone, or with both loneliness and frailty were associated with higher risk of cardiac events. When it comes to stroke, those with frailty alone, or with both loneliness and frailty had higher risk than those with robust (Table 2).
Figure 1.
Kaplan–Meier curves for the cumulative risk of CVD, stroke and cardiac events by frailty/loneliness status
Group 1 (Robust); group 2 (Loneliness only); group 3 (Frailty only); group 4 (Loneliness and Frailty).
Table 2.
Association of frailty and loneliness with new-onset CVD, stroke and cardiac events
| Robust | Loneliness only | Frailty only | Loneliness and Frailty | P trend | |
|---|---|---|---|---|---|
| CVD† | |||||
| Case, n (%) | 1029(15.99) | 364(18.93) | 167(25.97) | 198(29.51) | |
| Model 1a | 1.00(Ref) | 1.23(1.08–1.40) | 1.84(1.53–2.23) | 2.20(1.84–2.63) | <0.001 |
| Model 2b | 1.00(Ref) | 1.19(1.04–1.36) | 1.59(1.32–1.93) | 1.90(1.58–2.28) | <0.001 |
| Model 3c | 1.00(Ref) | 1.24(1.10–1.40) | 1.62(1.37–1.92) | 1.92(1.64–2.27) | <0.001 |
| Stroke | |||||
| Case, n (%) | 347(5.39) | 112(5.82) | 61(9.49) | 64(9.54) | |
| Model 1a | 1.00(Ref) | 1.08(0.88–1.34) | 1.79(1.36–2.35) | 1.81(1.39–2.37) | <0.001 |
| Model 2b | 1.00(Ref) | 1.08(0.87–1.33) | 1.59(1.20–2.10) | 1.64(1.25–2.15) | <0.001 |
| Model 3c | 1.00(Ref) | 1.15(0.90–1.39) | 1.65(1.25–2.19) | 1.65(1.24–2.19) | <0.001 |
| Cardiac events | |||||
| Case, n (%) | 768(11.93) | 279(14.51) | 122(18.97) | 155(23.10) | |
| Model 1a | 1.00(Ref) | 1.23(1.08–1.42) | 1.67(1.38–2.02) | 2.07(1.74–2.46) | <0.001 |
| Model 2b | 1.00(Ref) | 1.33(1.12–1.57) | 1.39(1.25–1.55) | 1.77(1.49–2.12) | <0.001 |
| Model 3c | 1.00(Ref) | 1.26(1.09–1.44) | 1.56(1.28–1.90) | 1.99(1.66–2.38) | <0.001 |
† CVD including stroke and cardiac events. a. Model 1 adjusted for age, sex; b. Model 2 further adjusted for living place, education level, smoking status, drinking status and BMI based on model 1; c. Model 3 further adjusted for hypertension, diabetes mellitus and dyslipidemia based on model 2.
In sensitivity analysis, participants were divided into four groups according to pre-frailty and loneliness. The significant association between loneliness/pre-frailty and risk of CVD, stroke and cardiac events were consistent with the main results (Table 3). As shown in Table S2 and Table S3, individuals with loneliness or frailty were associated with study outcomes. Furthermore, the coexistent of loneliness and frailty (according to frailty phenotype) conferred increased risk of CVD, stroke and cardiac events than each component individually among participants with age ≥ 60 years (Table S4). In age subgroups, individuals with both loneliness and frailty were significantly associated with increased risk of CVD, stroke and cardiac events among all age subgroups (< 60 years; ≥ 60 years), with the significant interaction between age subgroup and loneliness/frailty group (P-interaction<0.05) (Table 4). In other subgroup analysis, the significant associations between loneliness only, frailty only and loneliness/frailty with risk of CVD were observed in sex, living place smoking, drinking, diabetes mellitus and BMI subgroups (Table S5). Significant interactions between loneliness/frailty groups and subgroups were observed in BMI subgroups.
Table 3.
Sensitivity analysis the association of pre-frailty and loneliness with new-onset CVD, stroke and cardiac events
| Variable | Robust | Loneliness only | Pre-frailty only | Loneliness and pre-frailty | P trend |
|---|---|---|---|---|---|
| CVD† | |||||
| Case, n (%) | 371(13.36) | 72(16.82) | 825(19.17) | 490(22.62) | |
| Model 1a | 1.00(Ref) | 1.31(0.99–1.73) | 1.54(1.35–1.76) | 1.90(1.63–2.20) | <0.001 |
| Model 2b | 1.00(Ref) | 1.31(0.99–1.72) | 1.40(1.22–1.60) | 1.67(1.44–1.94) | <0.001 |
| Model 3c | 1.00(Ref) | 1.35(1.05–1.73) | 1.48(1.30–1.69) | 1.81(1.57–2.09) | <0.001 |
| Stroke | |||||
| Case, n (%) | 145(5.22) | 26(6.07) | 263(6.11) | 150(6.93) | |
| Model 1a | 1.00(Ref) | 1.17(0.77–1.77) | 1.17(0.96–1.43) | 1.34(1.06–1.68) | 0.016 |
| Model 2b | 1.00(Ref) | 1.19(0.78–1.80) | 1.09(0.90–1.34) | 1.32(1.01–1.65) | 0.046 |
| Model 3c | 1.00(Ref) | 1.26(0.83–1.91) | 1.14(0.92–1.41) | 1.30(1.02–1.67) | 0.043 |
| Cardiac events | |||||
| Case, n (%) | 258(9.29) | 53(12.38) | 632(14.69) | 381(17.59) | |
| Model 1a | 1.00(Ref) | 1.36(1.01–1.82) | 1.63(1.41–1.89) | 1.99(1.70–2.32) | <0.001 |
| Model 2b | 1.00(Ref) | 1.33(0.99–1.79) | 1.49(1.28–1.72) | 1.74(1.48–2.04) | <0.001 |
| Model 3c | 1.00(Ref) | 1.41(1.05–1.89) | 1.64(1.41–1.91) | 2.03(1.71–2.40) | <0.001 |
In sensitivity analysis, participants were divided into four groups according to pre-frailty and loneliness. t CVD including stroke and cardiac events. a. Model 1 adjusted for age, sex; b. Model 2 further adjusted for living place, education level, smoking status, drinking status and BMI based on model 1; c. Model 3 further adjusted for hypertension, diabetes mellitus and dyslipidemia based on model 2.
Table 4.
Association of frailty and loneliness with new-onset CVD, stroke and cardiac events by age subgroup
| Variable | Robust | Loneliness only | Frailty only | Loneliness and Frailty | P trend |
|---|---|---|---|---|---|
| Age < 60 years | |||||
| CVD† | 1.00(Ref) | 1.32(1.12–1.50) | 1.85(1.42–2.42) | 1.96(1.51–2.53) | <0.001 |
| Stroke | 1.00(Ref) | 1.21(0.90–1.64) | 1.94(1.23–304) | 1.60(1.01–2.63) | 0.002 |
| Cardiac events | 1.00(Ref) | 1.33(1.11–1.60) | 1.68(1.23–2.31) | 2.00(1.50–2.67) | <0.001 |
| Age ≥ 60 years | |||||
| CVDt | 1.00(Ref) | 1.14(0.96–1.37) | 1.46(1.17–1.81) | 1.90(1.55–2.33) | <0.001 |
| Stroke | 1.00(Ref) | 1.06(0.78–1.45) | 1.47(1.03–2.10) | 1.77(1.26–2.50) | <0.001 |
| Cardiac events | 1.00(Ref) | 1.17(0.95–1.44) | 1.46(1.13–1.88) | 1.97(1.56–2.49) | <0.001 |
† CVD including stroke and cardiac events. In the multivariate models, confounding factors such as age, sex, living place, education level, smoking status, drinking status, BMI, hypertension, diabetes mellitus, diabetes mellitus and dyslipidemia were included unless the variable was used as a subgroup variable.
Discussion
To our knowledge, this is the first study to examine the combined impact of frailty and loneliness on the development of CVD. In this nationwide prospective cohort study of Chinese adults aged 45 years and above, we found that individuals with loneliness or frailty were found to be independently associated with higher risk of CVD, stroke and cardiac events. Furthermore, the coexistent of loneliness and frailty conferred increased risk of CVD, stroke and cardiac events than each component individually, and the combined effect was independent of age, sex and other covariates. Our findings suggested a combination of loneliness and frailty could provide potentially predictive information for CVD incidence.
Results from the UK Biobank study of 479,054 participants followed for over 7 years found that loneliness had 1.49-fold and 1.36-fold increased risk of incident acute myocardial infarction and stroke (18). In a systematic review of 23 studies from 16 datasets with 4,628 CHD and 3,002 stroke events over 3 to 21 years, findings indicated that poor social relationship was associated with a 29% increase in risk of incident CHD and a 32% increase in risk of stroke, and the significant association was independent of age, sex and other potential conventional risk factors (32). To the best of our knowledge, our study is the first study on this topic among Chinese. Our results are in agreement with the previous studies where loneliness had been associated with increased CVD and cardiac events incidence. It reported that frailty occurs in as much as 50% of older patients with cardiovascular disease, and frailty and pre-frailty individuals had increased risk of CVD than their robust counterparts (33, 34). Evidence from UK Biobank study indicated frailty was associated with higher risks of CVD incidence, and led to a reduction in life expectancy (35). In consistent with previous study from CHARLS (24), our study showed that the frailty index which does not include any traditional CVD risk factors, is associated with CVD, stroke and cardiac events, and the association was independent of several traditional risk factors with CVD. Emerging evidence from population and epidemiology confirmed that both loneliness and frailty was causally associated with increased risk of CVD.
In aging population, loneliness and frailty are common and often coexist. Previous systematic review and meta-analysis had demonstrated a significant cross-sectional and longitudinal associations between loneliness and frailty (20). Emiel O et al found individuals with a combined presence of frailty and loneliness were at highest risk of mortality compared with older adults with frailty or loneliness, or without any of the conditions (36). In current study, we provided a more valid appraisal of the combined effect of frailty and loneliness, and results suggested a higher risk of CVD among those with both frailty and loneliness.
Frailty has often been thought to be associated with aging, and previous studies focused on the impact of frailty among individuals aged more than 60 or 65 years. Several studies have suggested that there is a stronger association between frailty and mortality among younger adults compared to that among older adults (37, 38). Another study from UK Biobank study also found the associations between frailty and CVD risk were stronger among women of age<45 and among men of 45≤age<60 (35). In present study, those with both loneliness and frailty were significantly associated with increased risk of CVD, stroke and cardiac events among those with age less or more than 60 years. Our study adds the evidence to previously reported studies on loneliness and frailty with risk of CVD among younger population.
The precise mechanisms linking loneliness and frailty with risk of CVD are incompletely understood but several potential explanations could be proposed. Both loneliness, frailty and CVD share some common risk factors, such as physical inactive, smoking and Improper diet (35). Furthermore, hypertension, diabetes, especially and abdominal obesity, may be shared between loneliness, frailty and CVD (39). Loneliness or frailty may have more direct physiological effects, expressed by neuroendocrine and immune responses (39, 40). However, further research is needed to better understand the precise mechanisms.
The current study was based on the data from the CHARLS study, which is a large nationally representative cohort study with a high response rate, and potential confounders were collected and controlled in the multivariable models. To data, our present study represents the first to confirm the combined effect of loneliness and frailty on CVD risk. Both loneliness and frailty are dynamic and its earlier stages are potentially reversible. Lifestyle interventions or friendship courses and psychological therapies have the potential to reduce weakness and diminish feelings of loneliness. Therefore, further research is needed to better understand the extent to which these changes may have functional benefits. However, the limitations in our study are worth noticing. First, both loneliness, frailty and CVD were self-reported, which may cause information bias. However, self-reported history of diseases has been proven to possess relatively good reliability. Second, the CHARLS study is exclusively a Chinese population, and the findings from our study might not be generalizable to other populations. Third, the information about heart failure and atrial fibrillation were not collected in CHARLS. Therefore, the bias caused by these factors cannot be ruled out. Fourth, there was a single assessment of triglycerides/cholesterol for dyslipidemia and fasting glucose for diabetes mellitus, which may cause bias. Finally, the present study was not a prespecified analysis. This observational analysis could be influenced by potential biases and confounding factors. Therefore, our study only generates hypotheses for future studies.
In conclusion, our findings indicated that there is a combined effect of loneliness and frailty on CVD, stroke and cardiac events risk among the middle-aged and elderly Chinese. As a simple and cost-saving tool, loneliness and frailty has great potential to enhance primary prevention. Our findings indicated a need to identify loneliness and frailty, and intervene much earlier. Incorporate loneliness and frailty screening into routine physical examinations may help identify patients with CVD and promote innovations in health management. Considering that loneliness and frailty might be a reversible syndrome, extending preventive efforts to younger people may effectively reverse loneliness and frailty and reduce CVD risk.
Acknowledgments
This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01 AG030153, RC2 AG036619, R03 AG043052). For more information, please refer to www.g2aging.org.
Authors' contributions:
Xiaowei Zheng and Jiawei Ma conceived and designed the research; Kaixin Zhang and Xiaowei Zheng wrote the manuscript; and Kaixin Zhang and Xiaowei Zheng performed the data analysis. All authors reviewed the manuscript.
Funding:
This work was supported by ‘Scientific Research Project of Wuxi Municipal Health Commission(Q202221)'.
Availability of data and materials:
This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data.
Ethics approval and consent to participate:
Not applicable.
Consent for publication:
Not applicable.
Competing interests:
The authors declare that they have no competing interests.
Sponsor's Role:
The sponsors had no role in the design, methods, data collection, analysis, or preparation of the manuscript.
Electronic Supplementary Material
Supplementary material is available in the online version of this article at https://doi.org/10.1007/s12603-023-2037-3.
Supplementary material, approximately 37.8 KB.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material, approximately 37.8 KB.
Data Availability Statement
This analysis uses data or information from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data.